Fast flow field prediction approach of supersonic inlet in wide operating range based on deep learning

Abstract

The flow in a supersonic inlet at wide operation range is quite complicated and contains complex shock reflections and shock wave/boundary layer interactions (SWBLI). So it is difficult to achieve the operating state monitoring in real-time by the sensor signals only. The recently booming deep learning (DL) is promising to provide a new way for flow field reconstruction due to its excellent data mining capability. For these, a fast flow field prediction model based on deep learning is proposed for the supersonic inlet in a wide operation range. To establish the data set for model training, numerical simulations at various Mach number and flight altitude obtained by Latin Hypercube Sampling (LHS) method are carried out. The trained model successfully established the mapping relationship between the inlet flow field and the input features. Both the start and unstart flows can be reconstructed by the model with high accuracy and efficiency. Then, two different types of input features are compared to test the influence of different input features on the model performance. The results indicated that the pressure distribution inputs perform better than that of operating conditions, which is because the wall pressure distribution contains more information and characteristics of the flow. Furthermore, compared with the traditional Computational Fluid Dynamics (CFD) simulations and wind tunnel experiments, the model has higher efficiency in predicting the inlet flow field with high accuracy. Finally, interpolation and extrapolation tests are conducted and the results revealed that the model is of high robustness and generalization ability.